Efficient Bark Recognition in the Wild

Rémi Ratajczak, Sarah Bertrand, Carlos Crispim-Junior, Laure Tougne

2019

Abstract

In this study, we propose to address the difficult task of bark recognition in the wild using computationally efficient and compact feature vectors. We introduce two novel generic methods to significantly reduce the dimensions of existing texture and color histograms with few losses in accuracy. Specifically, we propose a straightforward yet efficient way to compute Late Statistics from texture histograms and an approach to iteratively quantify the color space based on domain priors. We further combine the reduced histograms in a late fusion manner to benefit from both texture and color cues. Results outperform state-of-the-art methods by a large margin on four public datasets respectively composed of 6 bark classes (BarkTex, NewBarkTex), 11 bark classes (AFF) and 12 bark classes (Trunk12). In addition to these experiments, we propose a baseline study on Bark-101, a new challenging dataset including manually segmented images of 101 bark classes that we release publicly.

Paper Citation

in Harvard Style

Ratajczak R., Bertrand S., Crispim-Junior C. and Tougne L. (2019). Efficient Bark Recognition in the Wild. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 240-248. DOI: 10.5220/0007361902400248

in Bibtex Style

@conference{visapp19,
author={Rémi Ratajczak and Sarah Bertrand and Carlos Crispim-Junior and Laure Tougne},
title={Efficient Bark Recognition in the Wild},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP},
year={2019},
pages={240-248},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007361902400248},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 4: VISAPP
TI - Efficient Bark Recognition in the Wild
SN - 978-989-758-354-4
AU - Ratajczak R.
AU - Bertrand S.
AU - Crispim-Junior C.
AU - Tougne L.
PY - 2019
SP - 240
EP - 248
DO - 10.5220/0007361902400248
PB - SciTePress